There are several methods to generate a binaural audio signal from a multichannel signal that are based on a fixed filterbank structure. Some other variations include using a non-uniform filterbank structure or structures based on alternative auditory scales. Although binaural signals can be satisfactorily generated, such methods are not suitable to manipulating the components present within the audio signal. The spatial analysis of a multichannel signal is performed on a single band which may contain contributions from multiple auditory sources (i.e. a multipitch signal could have very closely spaced harmonics). It may not be possible to get the spatial distribution of the different components present in the entire spectrum of the signal. Performance of pitch synchronous analysis of such signals is restricted to signals containing a single pitch, since multipitch signals tend to be difficult to analyze and require complex algorithms.
Many signal processing applications require detecting a tone and estimating its location from a signal. Some examples where detection of tones from audio signal spectrum is required include sinusoidal modeling requiring detection of spectral peaks and psychoacoustic models requiring identification of tone and noise like components in spectrum to apply the appropriate masking rules. A voice signal is characterized by harmonic structure and detecting harmonicity in spectrum requires detection of tone. Further, most musical instruments produce sounds containing tonal structure (it could be harmonic or inharmonic). Alternative applications include detection of interfering tones or selecting tone from noisy background or estimation of periodicity.
Performance of tone detection methods can suffer due to noise. Some tonal component detection methods may require estimating approximate pitch in a time domain and then refining the spectral peak estimate in a spectral domain. In such scenarios, performance of pitch detection can degrade in the presence of multiple periodicities in the signal. Many techniques are based on distance measures or correlation based or geometrical and search based methods to detect the tones and require comparison with a threshold for some stage of decision making. Thresholds on spectral mismatches are prone to errors in the presence of noise and also need normalization based on signal strengths.